# Best practice to choose epoch or number of features from a recursive feature elimination process?

**URL:** https://discourse.julialang.org/t/best-practice-to-choose-epoch-or-number-of-features-from-a-recursive-feature-elimination-process/115281
**Category:** Machine Learning
**Created:** [June 6, 2024, 1:13pm UTC](https://discourse.julialang.org/t/best-practice-to-choose-epoch-or-number-of-features-from-a-recursive-feature-elimination-process/115281 "2024-06-06T13:13:23Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![liuyxpp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/liuyxpp/32/9870_2.png) [@liuyxpp](https://discourse.julialang.org/u/liuyxpp)
#### Post date: [June 6, 2024, 1:13pm UTC](https://discourse.julialang.org/t/best-practice-to-choose-epoch-or-number-of-features-from-a-recursive-feature-elimination-process/115281/1 "2024-06-06T13:13:23Z")

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In general, how to find the smallest number which corresponds to best score in a noisy curve? Below is an example result form a recursive feature elimination process. The x axis is the number of features selected and the y-axis is the well-known R2 metric.

 ![newplot](https://global.discourse-cdn.com/julialang/original/3X/e/9/e9bcf4330b7f9b0a8aa69849a38d59d24847abbc.png)

It seems either 65 or 80 is a good choice?